The power industry has been rapidly changing with the advent of deregulation as well as other socio-economic factors. As a result, increases in efficiency and control of power generation costs are becoming of more importance. To meet the industry needs, a large number of siloed information technology (IT) applications have been introduced. However, these applications are typically not built with integration in mind with each application being too proprietary in nature and specifically tailored for a particular power generation operation. Accordingly, collection and integration of data from these applications and systems are extremely difficult outside of the intended operation. Many utilities have sought to create a large scale data warehouse to solve this integration problem with very little success.
Another difficulty with prior art systems is the disparate number of locations even within the organization that needs access to the data. For example, within a power company, traders on a central trade floor, plant personnel at each power plant, engineers stationed regionally, management dispersed throughout the organization, and third parties all need access to the data in some form. The traditional siloed applications are typically client-server based applications and it is difficult to provide access to everyone in need of the data.
In addition, due to the generally isolated nature of the prior art systems as described above, combining qualitative event type data (e.g., real-time or recorded plant operations data) and quantitative data (e.g., Supervisory Control and Data Acquisition (SCADA) and market data) becomes difficult and cumbersome, if not impossible, due to the size and disparity of the data. On the other hand, such information is important in determining proper operation of power generation as back office settlement activities determine penalties associated with under or over production of power, for example. Typically, back office personnel manually extract data from a number of different IT systems in the organization to determine the activities that occurred in prior reporting periods. Many times, logs maintained in word processing or hand written documents must be searched manually.
Moreover, when a type of report is required, IT developers have to develop some level of custom code to extract data from the data and format the data properly onto a report. This task becomes even more complicated when disparate data sources with varying data formats are used.